The hedonic price method uses the value of a surrogate good or service to measure the implicit price of a non-market good. For example, house prices can be used to provide a value of particular environmental attributes. Individuals may be willing to pay a premium for a house located close to a country park, while they may wish to have a discount on a house which is located close to a open cast mining site.

House and other property prices are not simply determined by one variable as listed in the above examples. They are a product of a number of factors including:

Characteristics of the property.

Characteristics of the location.

Characteristics of the environment.

The hedonic price method is used to measure the relative importance – through use of regression analyses – of these independent ‘explanatory’ variables on house and property prices. If, for example, through regression analyses increased distance from an open cast mining site is found to be correlated with increased house prices, it can be ascertained that the open cast site is having a negative impact on house prices. The regression analysis can also be used to provide a value for the size of the relative impact. It may be found that a 1km movement away from the open cast site equates to an increase of £5,000 on a house price.

The hedonic regression analysis is conducted in two steps. The first step estimates the relationship between the price of an asset (the dependent variable) and all of its various characteristics (independent variables). For example, the price of a house can be summarised using a hedonic price function as below:

Where the price of a house (P), is a function of its location relative to a local urban centre (LOC), the type of house (TYPE), the size of the plot (SIZE), the quality of its view (VIEW), and neighbourhood characteristics (NEIGH) such as school quality and crime.

The change in a house price resulting from the marginal change in one of these characteristics is called the hedonic price (sometimes referred to as the implicit price or rent differential). The hedonic price can therefore be interpreted as the additional cost of purchasing a house that is marginally ‘better’ in terms of a particular characteristic.

Usually researchers estimating hedonic prices assume the hedonic price function has a multiplicative functional form. This means that as a characteristic increases (or improves) the house prices increase but at a decreasing rate. This is expressed in the following way:

Here the parameters β1 to β5 are elasticities. These parameters measure the proportional change in prices caused by proportional changes in characteristics. For example, we would expect β3 > 0 as house prices will increase as plot size increases. The hedonic price of a particular characteristic is therefore the slope of this equation with respect to that particular characteristic. For example, the hedonic price of plot size is expressed as:

The hedonic price of house sizes is dependent on the value of the parameter β3, the price of the house, and the size of the house. The hedonic price of a characteristic can be interpreted as the willingness to pay of households for a marginal increase in that particular characteristic.

The second step of the hedonic regression analysis estimates the willingness to pay of households but additionally accounts for households having different incomes and tastes. The willingness to pay function therefore becomes:

Where the willingness to pay for the size characteristic is dependent on size of the house (SIZE), income of the household (Y), and a vector (Z) which denotes tastes (based on age, race, social background, family size etc).

Problems with Hedonic Models

There are a number of limitations in the use of the hedonic pricing method. These include:

(1) Information: the model requires that all individuals have prior knowledge of the potential positive and negative externalities they may face having purchased a house. For example, they should have prior knowledge of the level of pollution an open cast mining site will cause and how this will affect them. Of course in reality this is not always the case.

(2) Measurement validity: the quality of the measures used in the independent ‘explanatory’ variables is of key importance. If proxy measures are used, for example for the build quality of a house, this could result in an inaccurate coefficient being generated in the regression analyses.

(3) Market limitations: the model ideally requires that a variety of different houses are available so that individuals are able to obtain the particular house of their choosing, with a combination of characteristics they desire. However, in reality it may be the case that a family wishing to purchase a large house with a garden in a busy city centre location, may find that the city centre only contains small houses, or houses without gardens.

(4) Multicollinearity: it may be the case that large houses are only found in green areas with low pollution, and small houses are only found in urban areas with high pollution. In this case it would be impossible to separate out pollution and house size accurately.

(5) Price changes: the model assumes that market prices adjust immediately to changes in attributes. In reality there will likely be a lag associated with this, especially in areas where house sales and purchases are rare.

This resource was created by Dr Dan Wheatley. The project was funded by the Economics Network and the Centre for Education in the Built Environment (CEBE) as part of the Teaching and Learning Development Projects 2010/11.